Learning Chinese Word Embeddings By Discovering Inherent Semantic Relevance in Sub-characters-Reference-Cited by-同舟云学术

Learning Chinese Word Embeddings By Discovering Inherent Semantic Relevance in Sub-characters

Published:2022-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Information & Knowledge Management
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Author:

Lu Wei¹,Zhang Zhaobo¹,Yuan Pingpeng¹,Jin Hai¹,Hua Qiangsheng¹

Affiliation:

1. Huazhong University of Science and Technology, Wuhan, China

Funder

National Natural Science Founda- tion of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3511808.3557376

Reference31 articles.

1. Enriching Word Vectors with Subword Information

2. Shaosheng Cao , Wei Lu , Jun Zhou , and Xiaolong Li . 2019 . cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information . In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 5053--5061 . Shaosheng Cao, Wei Lu, Jun Zhou, and Xiaolong Li. 2019. cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 5053--5061.

3. Radical Enhanced Chinese Word Embedding

4. An Effective Non-Autoregressive Model for Spoken Language Understanding

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